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| Quantitative Evaluation and Optimization of GAI Policy and Regulation Texts' Focus on Information Governance |
| Wang Xu1, Liu Binbin1, Qiu Junping2 |
1.School of Economics and Management, Yanshan University, Qinhuangdao 066004
2.Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, Hangzhou 310018 |
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Abstract In the digital and intelligent era, generative artificial intelligence (GAI) has been garnering worldwide attention. The emergence of large language models has triggered many chaotic phenomena in the information ecology. The quantitative evaluation and optimization of GAI policies and regulations have practical significance in promoting the study of GAI governance rationalization, facilitating the enhancement of the level of social risk management and the effectiveness of information governance, and advancing national cyberspace governance. First, this study analyzes the information governance dilemma triggered by GAI content. Second, it employs the policy modeling consistency (PMC) index model method and combines it with the MatLab tool to quantitatively evaluate and analyze the texts of 14 global GAI policies and regulations. The findings reveal that the overall level of consistency of policies and regulations is relatively good, but there are still problems with unclear types of services in legislative industries and areas, restricted application functions of trustworthiness and controllability, and solidification of the scope of technical safeguard governance. Accordingly, this study proposes four optimization dimensions—technology optimization, risk assessment, application deployment, and international policy and regulation integration. Owing to the information governance dilemma triggered by GAI, the study refines agile governance into three core dimensions—intelligent services, trusted applications, and technical security—which serve as a yardstick for evaluating GAI policies and regulations. It constructs an optimization framework of flexible solutions and scenario-based hierarchical governance modes with both soft and hard methods, and proposes an optimization proposal of GAI policies and regulations oriented to information governance.
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Received: 11 March 2024
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